<p>The three essays focus on important facets of R&amp;D such as the impact of competition, regulation and the difficulties in measuring R&amp;D and the dollars to be allocated to R&amp;D by a firm. In the first essay, we investigate Aghion etal. (2005), model on the relationship between R&amp;D and competition. We identify limitations in previous empirical tests of the model. Further, R&amp;D appropriability plays no role in the model although literature assigns it a significant role. Our comprehensive tests reveal that the model does not fully explain the R&amp;D-competition relationship, and the results depend on the competition measure used. We investigate the role R&amp;D appropriability and confirm its significance. Hence the model needs refinements. This study enhances our knowledge of the role competition and R&amp;D appropriability play in enhancing R&amp;D and helps formulate policies that promote R&amp;D. </p><p> In the second essay, we analyze the changes in pharmaceutical firms' stock prices following the recall of Vioxx, Merck's blockbuster drug, apropos three theories based on government regulation, product liability, and firms' reputations. We conduct an event study of estimated abnormal share returns using a Fama-French 3 factor model under seemingly unrelated regression (SUR) estimation. Our investigations support government regulation theory and suggest that R&amp;D-intensive firms suffer maximum adverse returns. Adverse returns reflect anticipated regulatory changes in approvals for new drugs. Drug recall harms the industry and future availability of new drugs. Stable and fair drug approvals policy can help the industry flourish. </p><p> The third essay critically examines measurement of R&amp;D. R&amp;D capital and cited patents are used in the literature to measure R&amp;D intensity and investigate market returns for R&amp;D. The results are ambiguous. Previous literature suggests that patents are distinct from R&amp;D expenditure, and R&amp;D is influenced by competition. We suggest eight new measures based on the interplay between R&amp;D and competition. We empirically test these measures on pharmaceutical and computer software industries which have the highest R&amp;D intensities of all industries. The new measures are more significant than R&amp;D capital and offer further insights on R&amp;D in these industries. These measures help in capital allocation for R&amp;D at firm level which maximizes stock returns. </p>

<p> The study analyzed survey data and investment returns from 477 angel investment ventures to evaluate factors related to financial rates of return. Principal components analysis, linear regression, and nonparametric methods were used to reduce and analyze the data. The findings indicated that only the angel investor&rsquo;s perception of market risk was related to the rates of return on their investment, and that the rates of return to the angel investors was superior to alternative investments in common market indexes. Logistic regression was used to construct a model that increased predicted angel investment break even by 50% over chance.</p>

<p> Participants in real-estate transactions involving the purchase of single-family homes consider a variety of factors when establishing a price. These factors include crime, interest rates, income, unemployment, quality of schools, and many other factors. The current quantitative, correlational and multiple regression analysis study examined the empirical relationships among price, crime, interest rates, income, unemployment, and schools in Prince George&rsquo;s County, Maryland, by examining the contribution of each independent variable to the dependent variable price. Findings indicated that income and unemployment have no significant relationship to price and do not contribute significantly to price of single-family homes. Interest rates, total crime, and school average SAT scores have a significant relationship with price and contribute individually and collectively to the final sales price of single-family homes in Prince George&rsquo;s County, Maryland. These findings support further research into the relationship and contribution of other independent variables to the price established for single-family homes. The study results have potential implications for the real-estate industry, private-sector leaders, and government officials to improve their leadership effectiveness in managing this aspect of the economy. </p>

A branch-and-bound algorithm for the solution of a class of mixed-integer nonlinear programming problems arising from the field of investment portfolio selection is presented. The problems in this class are characterized by the inclusion of the fixed transaction costs associated with each asset, a constraint that explicitly limits the number of distinct assets in the selected portfolio, or both. Modeling either of these forms of limiting the cost of owning an investment portfolio involves the introduction of binary variables, resulting in a mathematical programming problem that has a nonconvex feasible set. Two objective functions are examined in this thesis; the first is a positive definite quadratic function which is commonly used in the selection of investment portfolios. The second is a convex function that is not continuously differentiable; this objective function, although not as popular as the first, is, in many cases, a more appropriate objective function. To take advantage of the structure of the model, the branch-and-bound algorithm is not applied in the standard fashion; instead, we generalize the implicit branch-and-bound algorithm introduced by Bienstock (3). This branch-and-bound algorithm adopts many of the standard techniques from mixed-integer linear programming, including heuristics for finding feasible points and cutting planes. Implicit branch-and-bound involves the solution of a sequence of subproblems of the original problem, and thus it is necessary to be able to solve these subproblems efficiently. For each of the two objective functions, we develop an algorithm for solving its corresponding subproblems; these algorithms exploit the structure of the constraints and the objective function, simplifying the solution of the resulting linear systems. Convergence for each algorithm is proven. Results are provided for computational experiments performed on investment portfolio selection problems for which the cardinality of the universe of assets available for inclusion in the selected portfolio ranges in size from 52 to 1140.

Electricity demand analysis using cointegration and error-correction models with time varying parameters: The Mexican case. In this essay we show how some flexibility can be allowed in modeling the parameters of the electricity demand function by employing the time varying coefficient (TVC) cointegrating model developed by Park and Hahn (1999). With the income elasticity of electricity demand modeled as a TVC, we perform tests to examine the adequacy of the proposed model against the cointegrating regression with fixed coefficients, as well as against the spuriousness of the regression with TVC. The results reject the specification of the model with fixed coefficients and favor the proposed model. We also show how some flexibility is gained in the specification of the error correction model based on the proposed TVC cointegrating model, by including more lags of the error correction term as predetermined variables. Finally, we present the results of some out-of-sample forecast comparison among competing models. Electricity demand and supply in Mexico. In this essay we present a simplified model of the Mexican electricity transmission network. We use the model to approximate the marginal cost of supplying electricity to consumers in different locations and at different times of the year. We examine how costs and system operations will be affected by proposed investments in generation and transmission capacity given a forecast of growth in regional electricity demands. Decomposing electricity prices with jumps. In this essay we propose a model that decomposes electricity prices into two independent stochastic processes: one that represents the "normal" pattern of electricity prices and the other that captures temporary shocks, or "jumps", with non-lasting effects in the market. Each contains specific mean reverting parameters to estimate. In order to identify such components we specify a state-space model with regime switching. Using Kim's (1994) filtering algorithm we estimate the parameters of the model, the transition probabilities and the unobservable components for the mean adjusted series of New South Wales' electricity prices. Finally, bootstrap simulations were performed to estimate the expected contribution of each of the components in the overall electricity prices.

Canada's fragmented, provincially-based securities regulatory system is facing domestic and international pressures to become more coherent and efficient. This paper outlines various factors and proposals, concluding that the system must become nationally-based, but only if the change is properly planned, implemented and administered. There should be uniform (or, at least, coordinated) legislation, with federal and provincial joint delegation to a single commission. Interprovincial coordination must improve before, during and after the change. While feasibility requires most provinces to participate, the scheme should not be rejected if unanimity is lacking. Although important, regional autonomy cannot be allowed to outweigh national authority. Market participants will be somewhat reassured if presented with a realistic transitional plan and definite time-table. A national system should proceed only if the federal and provincial governments can plan and implement it with common sense and without damaging compromises.

<p> This dissertation focuses on two aspects of factor models, testing and forecasting. For testing, we investigate a more general high-dimensional testing problem, with an emphasis on panel data models. Specifically, we propose a novel technique to boost the power of testing a high-dimensional vector against sparse alternatives. Existing tests based on quadratic forms such as the Wald statistic often suffer from low powers, whereas more powerful tests such as thresholding and extreme-value tests require either stringent conditions or bootstrap to derive the null distribution, and often suffer from size distortions. Based on a screening technique, we introduce a ''power enhancement component", which is zero under the null hypothesis with high probability, but diverges quickly under sparse alternatives. The proposed test statistic combines the power enhancement component with an asymptotically pivotal statistic, and strengthens the power under sparse alternatives. As a byproduct, the power enhancement component also consistently identifies the elements that violate the null hypothesis. </p><p> Next, we consider forecasting a single time series using many predictors when nonliearity is present. We develop a new methodology called sufficient forecasting, by connecting sliced inverse regression with factor models. The sufficient forecasting correctly estimates projections of the underlying factors and provides multiple predictive indices for further investigation. We derive asymptotic results for the estimate of the central space spanned by these projection directions. Our method allows the number of predictors larger than the sample size, and therefore extends the applicability of inverse regression. Numerical experiments demonstrate that the proposed method improves upon a linear forecasting model. Our results are further illustrated in an empirical study of macroeconomic variables, where sufficient forecasting is found to deliver additional predictive power over conventional methods.</p>

<p> This mixed methods study was to examine coaches who provided coaching for leaders to improve employee career development, defined as the individual's involvement and satisfaction with the organization in achieving his or her goals (Harter, Schmidt, &amp; Haynes, 2002). The purpose is to determine if these coaches are able to be evaluated through assessments to determine who is more effective in coaching leaders in the financial services industry, and to determine the overall effectiveness in working with leaders to determine a non-traditional return on investment that an organization can use to measure coaching. One way to measure a coaching outcome is by goal achievement (Spence, 2007). The individual will be able to determine if measureable progress is being made toward goal achievement, which allows for earlier assessment of whether or not coaching is successful. This study was implemented to find out earlier if the coaching is working and to develop a more systemic way to assist high potential executives rather than leaving it up to each individual coach. The research creates a survey instrument and pilots its use in a financial services organization to evaluate the effectiveness of the questionnaire set created to conduct this study.</p>

<p> This dissertation consists of three essays studying the role of accounting data in predicting distributions of stock returns. In the first essay, I explore the ability of accruals to predict future price (earnings) crashes and jumps, representing extreme negative and positive observations in the distribution of firm-level weekly returns (changes in quarterly ROA). I find that high (low) accruals predict a higher probability of price and earnings crashes (jumps) than medium accruals. In the second essay, I re-examine the ability of asset turnover growth, which reflects growth in both assets and sales, to predict future stock returns. While the prevailing view is that this relation is due to the spread between sales and asset growth, my results suggest it is driven mainly by the asset growth component. I do, however, find that this spread is positively related to future returns for a subsample of firms that did not make significant acquisitions or divestitures. In the third essay, I re-examine the puzzling negative correlation between aggregate stock returns and aggregate earnings at the quarterly level. I find that the negative aggregate returns-earnings correlation is unstable and the negative correlation for the period of 1976-2000 is mainly caused by the negative correlation between aggregate earnings and discount rate news.</p>

Thesis (Ph.D.)--Indiana University, Kelley School of Business, 2007. / Source: Dissertation Abstracts International, Volume: 68-09, Section: A, page: 4005. Advisers: Gregory F. Udell; Xiaoyun Yu. Title from dissertation home page (viewed May 5, 2008).